Abstract
Image set classification has drawn much attention due to its promising performance to overcome various variations. Recently, the point-to-point distance-based methods have achieved the state-of-the-art performance by leveraging the distance between the gallery set and the probe set. However, there are two drawbacks that need to be defeated: 1) they do not fully exploit the discrimination that exists between different gallery sets; 2) they face the great challenge of high computational complexity as well as multi-parameters, usually caused by some obvious sparse constraints. To address these problems, this paper proposes a novel method, namely latent sparse discriminative learning (LSDL), for face image set classification. Specifically, a new term is proposed to exploit the relations between different gallery sets, which can boost the set discrimination so as to improve performance. Moreover, we use a latent sparse constraint to reduce the trade-off parameters and computational cost. Furthermore, an efficient solver is proposed to solve our LSDL. Experimental results on three benchmark datasets demonstrate the advantages of our propose.
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Acknowledgements
This research was supported by the Intelligent Manufacturing and Robot Special Project of Major Science and Technology Project in Sichuan (Grant no. 2020ZDZX0014), the Science and Technology Special Project of Major Scientific Instruments and Equipment Project in Sichuan (Grant no. 19ZDZX0119), the Undergraduate Innovation and Entrepreneurship Project of Sichuan Province of Sichuan (Grant no. 19xcy099).
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Sun, Y., Ren, Z., Yang, C., Lei, H. (2020). Latent Sparse Discriminative Learning for Face Image Set Classification. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2020. Communications in Computer and Information Science, vol 1265. Springer, Singapore. https://doi.org/10.1007/978-981-15-7670-6_13
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DOI: https://doi.org/10.1007/978-981-15-7670-6_13
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